Why Hyperparameter Tuning ?
Hyperparameter tuning is an essential step in developing accurate and effective machine learning models. Here are some key reasons why hyperparameter tuning is so important:
- Improved accuracy: Hyperparameter tuning can help improve the accuracy of machine learning models by finding the optimal values for the hyperparameters. Hyperparameters are parameters that are not learned during training, but rather set prior to training. These can include things like learning rate, regularization strength, and the number of hidden layers in a neural network.
2. Faster training and prediction: By finding the optimal values for the hyperparameters, hyperparameter tuning can also help speed up the training and prediction times for machine learning models.
3. Reduced overfitting: Hyperparameter tuning can help reduce overfitting in machine learning models by finding the optimal values for the hyperparameters that balance bias and variance.
4. Improved generalization: Finally, hyperparameter tuning can also help improve the generalization of machine learning models by finding the optimal values for the hyperparameters that generalize well to new data.
In conclusion, hyperparameter tuning is a critical step in developing accurate and effective machine learning models. By finding the optimal values for the hyperparameters, hyperparameter tuning can improve the accuracy, speed, generalization, and prevent overfitting in machine learning models.
